Instructions to use zhihan1996/DNA_bert_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhihan1996/DNA_bert_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="zhihan1996/DNA_bert_6", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("zhihan1996/DNA_bert_6", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("zhihan1996/DNA_bert_6", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
b054dd3
1
Parent(s): a794b19
Update dnabert_layer.py
Browse files- dnabert_layer.py +6 -0
dnabert_layer.py
CHANGED
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@@ -5,6 +5,7 @@ import torch.nn as nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.models.bert.modeling_bert import BertModel as TransformersBertModel
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from transformers.models.bert.modeling_bert import BertForMaskedLM as TransformersBertForMaskedLM
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from transformers.models.bert.modeling_bert import BertPreTrainedModel
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from transformers.modeling_outputs import SequenceClassifierOutput
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@@ -17,6 +18,11 @@ class BertForMaskedLM(TransformersBertForMaskedLM):
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def __init__(self, config):
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super().__init__(config)
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class DNABertForSequenceClassification(BertPreTrainedModel):
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def __init__(self, config):
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.models.bert.modeling_bert import BertModel as TransformersBertModel
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from transformers.models.bert.modeling_bert import BertForMaskedLM as TransformersBertForMaskedLM
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from transformers.models.bert.modeling_bert import BertForPreTraining as TransformersBertForPreTraining
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from transformers.models.bert.modeling_bert import BertPreTrainedModel
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from transformers.modeling_outputs import SequenceClassifierOutput
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def __init__(self, config):
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super().__init__(config)
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class BertForPreTraining(TransformersBertForPreTraining):
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def __init__(self, config):
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super().__init__(config)
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class DNABertForSequenceClassification(BertPreTrainedModel):
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def __init__(self, config):
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